论文标题
在路径计划期间，有价值的理由是占用网格图的不确定性吗？
Is it Worth to Reason about Uncertainty in Occupancy Grid Maps during Path Planning?
论文作者
论文摘要
本文调查了关于路径计划中不确定障碍的不确定存在的有用性，这通常源于概率的占用网格图在通过像立体声摄像机（如立体声摄像机）绘制绘制的概率占用网格图来表示环境。传统的规划范式使用占用概率的硬门槛规定了宣布单元是障碍，并相应地计划一条路径，同时将一条路径计划在同时将未知空间视为免费的空间。我们将这种方法与一种新的不确定性意识计划者进行了比较，该方法计划了两个不同的路径假设，然后将其最初的轨迹段合并为一个以“下一最佳视图”姿势结束的单个段。在采取了这种信息的观点之后，计划者提出了其中一个假设，或者如果即将发生碰撞，则提出了全新的假设。进行了模拟，比较了提议的和传统的计划者。结果表明，计划场景的存在 - 例如，当环境包含死胡同或将目标放置在接近障碍的情况下 - 关于不确定性的推理可以大大降低机器人的行进距离并增加达到目标的机会。这位新的计划者还通过配备了ZED 2立体声摄像机的真实Clearpath Jackal机器人进行了验证。
This paper investigates the usefulness of reasoning about the uncertain presence of obstacles during path planning, which typically stems from the usage of probabilistic occupancy grid maps for representing the environment when mapping via a noisy sensor like a stereo camera. The traditional planning paradigm prescribes using a hard threshold on the occupancy probability to declare that a cell is an obstacle, and to plan a single path accordingly while treating unknown space as free. We compare this approach against a new uncertainty-aware planner, which plans two different path hypotheses and then merges their initial trajectory segments into a single one ending in a "next-best view" pose. After this informative view is taken, the planner commits to one of the hypotheses, or to a completely new one if a collision is imminent. Simulations were conducted comparing the proposed and traditional planner. Results show the existence of planning scenarios -- like when the environment contains a dead-end, or when the goal is placed close to an obstacle -- in which reasoning about uncertainty can significantly decrease the robot's traveled distance and increase the chances of reaching the goal. The new planner was also validated on a real Clearpath Jackal robot equipped with a ZED 2 stereo camera.
